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🧭 Contributor Onboarding Guide

ADIA Structural Break Challenge — Public Solution Architecture (CLv9-ECA Restricted Subset)

Welcome to the Algoplexity contributor guide for the ADIA Structural Break Detection challenge. This document introduces the architecture of our solution, describes each component’s role, and provides clear entry points for you to contribute meaningfully — without requiring access to proprietary symbolic–latent research.

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📦 System Overview

We model structural breaks in univariate time series using a two-stage deep learning pipeline:

1. Pre-train a dynamics-aware encoder on synthetic symbolic time series data derived from Elementary Cellular Automata (ECA).
2. Fine-tune the encoder on labeled real-world time series data to detect breakpoints.

This results in a robust model that generalizes well to unseen structural regimes.

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🧱 Architectural Layers

🟨 Notebook as a Self-Contained System

The entire solution runs as a single Python notebook executed by the ADIA Challenge Platform. It contains two callable entry points:

• train(X_train, y_train, model_dir)
• infer(X_test, model_dir)

The platform calls these functions and expects infer() to yield a sequence of scalar break predictions.

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🧩 Major Components (C4 Level 2 Summary)

Component	Role
Training Pipeline	Controls pre-training on ECA data and fine-tuning on real data.
Inference Pipeline	Loads the trained encoder and computes prediction scores from test data.
Core Library	Houses reusable modules: symbolic processors, model architectures, and encoders.
Model Store	Filesystem layer used to save and load encoder weights and configuration.

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🔄 Workflow Summary

📈 train() Function

1. Pre-Training Stage

   • Generate synthetic symbolic sequences using ECA rules.
   • Train a DynamicalAutoencoder with a reconstruction + classification loss.
   • Learn generalizable symbolic dynamics.

2. Fine-Tuning Stage

   • Process labeled real time series using SeriesProcessor.
   • Train a StructuralBreakClassifier to distinguish pre- and post-boundary regions using the encoder.
   • Save final model weights and config to model_dir.

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📉 infer() Function

1. Load encoder weights and config via EncoderLoader.

2. For each test time series:

   • Transform pre- and post-boundary segments using SeriesProcessor.
   • Use Fingerprinter to generate high-level vector encodings.
   • Use BreakScoreCalculator to compute the distance between pre- and post-fingerprints (e.g. cosine distance).
3. Yield the score.

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🛠️ Component-Level Roles (C4 Level 3 Highlights)

🔧 Core Services Library

Class/Module	Purpose
PermutationSymbolizer	Converts numeric series into symbolic ordinal patterns
SeriesProcessor	Transforms full time series into windows of symbolic sequences
TransformerEncoder	Learns vector representations (fingerprints) from sequences
DynamicalAutoencoder	Encodes + decodes sequences for unsupervised learning
StructuralBreakClassifier	Binary classifier predicting breaks from two fingerprints

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⚙️ Training Pipeline

Class	Description
ECADataGenerator	Produces labeled symbolic sequences using chaotic ECA rules
MDLPreTrainer	Trains the DynamicalAutoencoder using a dual-loss signal
BreakClassifierFinetuner	Fine-tunes the break classifier on real labeled data
EncoderSaver	Persists model weights and hyperparameters

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⚙️ Inference Pipeline

Class	Description
EncoderLoader	Loads model and config from Model Store
Fingerprinter	Uses SeriesProcessor + encoder to create symbolic fingerprints
BreakScoreCalculator	Compares before/after fingerprints to produce final break score

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🧪 Data Format (as provided by ADIA)

• X_train: pd.DataFrame with MultiIndex [id, time], columns: value, period
• y_train: pd.Series with id → bool indicating break presence
• X_test: List[pd.DataFrame], one per time series

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🎯 Contribution Opportunities

You are welcome to contribute to the following areas without IP conflict:

1. Improve ECA Sampling Logic

   • Try alternate chaotic rules
   • Vary simulation width or depth

2. Experiment with Alternative Fingerprinting Methods

   • Swap TransformerEncoder with simpler architectures (e.g., GRU, CNN)

3. Enhance Preprocessing

   • Try Z-score normalization or smoothing in SeriesProcessor

4. Tune Classifier Heads or Loss Functions

   • Replace BCE with focal loss or hinge loss

5. Modularization / Engineering Improvements

   • Improve logging, error handling, or reproducibility features

6. Add Test-Time Augmentations

   • Add dropout, ensemble averaging, or segment perturbations

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📎 Final Notes

• Please fork the project from the algoplexity.github.io notebook template or request edit access via the internal repo.
• Include comments on any experimental changes to allow reproducibility.
• Contributions will be reviewed based on clarity, performance gains, and maintainability.